1. Field of the Invention
This invention relates to the general subject of reducing noise in seismic and seismic derived rock property data; deriving various new data from input seismic and seismic derived rock property data which highlight spatial changes in subsurface structure, stratigraphy, lithology and rock fluids; and to the analysis and interpretation of such data.
2. Description of the Prior Art
Seismic data is acquired to provide information about the subsurface structure, stratigraphy, lithography and fluids contained in the rocks. Acquired seismic data records are the response of a seismic energy source after passing through and being reflected by rocks in the subsurface. Seismic data can be acquired at or close to the earth""s surface or can be acquired along boreholes. After acquisition, seismic data is typically processed to a set of seismic traces, where each trace represents the seismic response at a certain surface x,y location. The trace itself consists of a series of samples of the seismic response, usually ordered to correspond to increasing seismic travel time or, after depth conversion, increasing depth. Dependent on the acquisition geometry, the seismic traces are usually processed and organized to form lines with regularly spaced traces along the surface. The seismic data along such lines can be viewed as sections through the earth. Seismic data is referred to as 2D seismic data when the lines are in different directions or are far apart relative to the spacing of the traces. Seismic data is referred to as 3D seismic data when the acquisition is such that the processing results in a set of seismic lines that are organized sequentially and where the x,y trace locations form a regular grid and such that the spacing of the seismic lines generally is within the same order of magnitude as the spacing of the traces within the lines. In practice, the lines along which the data is acquired are called inlines and lines orthogonal to the inlines are referred to as crosslines. FIG. 1 shows a seismic section with a number of seismic data traces taken from the 3D seismic data cube of which the x,y grid is shown in FIG. 2. 2D and 3D seismic data sets are subsequently analyzed and interpreted, generally on computer workstations with specialized software, to reveal the subsurface structure, stratigraphy, lithography and fluids, and to so predict the location, structure, stratigraphy, lithology and fluid distribution of hydrocarbon reservoirs, associated aquifers and other subsurface features of interest. FIG. 3 shows a structural interpretation of the seismic data of FIG. 1. This interpretation delineates the overall reservoir zone, within which high seismic amplitudes correlate to oil sands. The interpretation also shows structural and stratigraphic relationships. Structural relationships typically relate to faulting, for example in FIG. 3 the interpretation shows how the layers defined by the horizons are broken up by the faults. Stratigraphic relationships typically relate to deposition and erosion. For example an interpretation may show how an erosional surface truncates lower lying layers.
The amplitudes of the seismic data are primarily determined by the strength of the reflection of seismic waves at layer boundaries. The reflection strength in turn is determined by changes in certain physical parameters of the rocks when going from one layer to the next. These physical parameters are determined by the physical properties of the rock matrix, i.e. the rock with empty rock pores, and fluids contained in the pores, jointly referred to as xe2x80x98rock property dataxe2x80x99. Changes in the rock matrix can be caused by changes in the lithology (rock mineral composition and build-up). Changes in fluids arise from changes in fluid type: water, oil and gas; or changes in properties of the fluid types. Using modern computer algorithms, rock property data can be estimated from the amplitudes of the seismic data. Rock property data which may be directly estimated from seismic data includes acoustic impedance, elastic impedance, pressure wave velocity, shear wave velocity and density. Further rock property data can also be drived directly or indirectly using functional, statistical or other relationships between the different rock properties. Seismic derived rock property data can be directly used to analyze changes in lithology and fluids in layers. Also, information about structure and stratigraphy is maintained and often even enhanced relative to seismic data. Use of seismic derived rock property data in subsurface analysis and interpretation is therefore often preferred over the use of seismic reflection data, or is done in conjunction with seismic data subsurface analysis and interpretation. For the same reason the subject method is preferably applied to seismic derived rock property data. FIG. 4 shows the same section as FIG. 1, but now shows a section through an acoustic impedance rock property cube derived from the seismic reflection data. Changes in acoustic impedance result in changes of seismic reflection coefficients. In other words, acoustic impedance is a layer property whereas the seismic reflection coefficients relate to the layer interface. Analysis of the difference between the seismic data and the acoustic impedance data reveals that oil bearing sands and their boundaries can be more accurately interpreted from the acoustic impedance data than from the seismic data itself.
To characterize an interpreted horizon or fault plane the dip and azimuth may be calculated at each horizon point. As illustrated in FIG. 5, the dip at an horizon point is the angle from the vertical to the gradient vector of a plane tangent to the horizon surface at the horizon point. The azimuth is the angle of the projection of the gradient vector on a horizontal plane calculated clockwise generally relative to North.
One key aspect of seismic and seismic derived rock property data is that generally this data does not contain sufficient information to at each sample determine all the required data about the structure, stratigraphy, lithology and fluid at that sample. Additional information is provided by analyzing and interpreting spatial variations in the seismic and seismic derived rock property data. For example, from the character of a spatial change it can be determined if the change is due to a change in structure, e.g. a fault, or due to a change in lithology or in fluids. The problem is that the information about the spatial variations is often not easily discerned or readily quantified from the seismic or seismic derived rock property data. This motivates the need for methods which generate data which highlight spatial changes in subsurface structure, stratigraphy, lithology and fluids, and for methods to analyze and interpret such data.
Methods have been described which focus on calculating certain measures of spatial discontinuity using only seismic data. These methods do not utilize the information captured in an interpretation of the seismic data. The proposed method departs from existing methods by employing a subsurface model, based on an available interpretation, to drive the calculation of new types of measures of spatial changes in subsurface structure, stratigraphy, lithology and fluids. These measures are derived from changes in the amplitudes of seismic data or seismic derived rock property data along horizons. One such measure is the gradient of the amplitudes, for distinction with the gradient of a geometric surface, referred to as the property gradient. This property gradient is determined at each horizon point by fitting a surface through amplitudes at the horizon point and surrounding horizon points, and calculating the gradient of this surface. Large gradients correspond to rapid lateral changes. An alternative method to characterize the amplitude changes is by smoothing the amplitude data along the horizon by filtering, and then taking the difference of the filtered and input data as measure of the rate of change of the amplitudes. Such filtering operations also reduce noise, and as such provide a new way to reduce noise in the input seismic and seismic derived rock property data.
The present invention provides a new and improved process to reduce noise in seismic and seismic derived rock property data; to generate data revealing information about the spatial variation of subsurface structure, stratigraphy, lithology and fluid content from seismic and seismic derived frock property data; and to analyze and interpret that data. The method uses as input seismic or seismic derived rock property data and an interpretation of this data. From the interpretation a subsurface model, in the following referred to as an earthmodel is built. The process calculations are driven by this earthmodel. In brief, the main steps for the 3D version of the process are:
Obtain seismic or seismic derived rock property data; interpret this data to define the horizons and faults which determine boundaries of layers of interest; from the horizons and faults and stratigraphic and structural relationships between the horizons and faults build an earthmodel where the input horizons and faults form the boundaries of the earthmodel main layers; guided by the stratigraphic and structural relationships, subdivide each earthmodel main layer into microlayers bounded by microlayer horizons defined at seismic or seismic derived rock property x,y grid points such that the microlayer horizons define the internal structure of each of the earthmodel main layers; for each grid point of each microlayer horizon find the spatial coordinates of a set of surrounding grid points on the microlayer horizon, which together define a microlayer horizon surface segment; rotate the microlayer horizon surface segment in the inline and crossline direction over a user defined angle range with a user defined step size around the current definition point to define the spatial coordinates of the rotated microlayer horizon surface segment; for this rotated microlayer horizon surface segment extract the corresponding seismic or seismic derived rock property data amplitudes; from these extracted amplitudes calculate one or multiple measures for the rate of change of these amplitudes along the current rotated microlayer horizon segment by calculating the size of the gradient or using various filters, the direction of the gradient, and the filter outputs; repeat for all angles in both the inline and crossline direction; for each rate of change measure determine the angles where the rate of change measure is minimum and for those angles store as output data the rotation angle dip and azimuth, the rate of change measure, the gradient direction and filter outputs; repeat this process for all grid points on each microlayer horizon and for all microlayer horizons; output the results in the form of a set of microlayer horizons where each microlayer horizon point contains the corresponding process output data; generate further output by interpolating the microlayer horizons to the seismic and seismic derived rock property grids; analyze and interpret the output data containing the information on spatial changes to predict lateral variations in substrate structure, stratigraphy, lithology and fluid distribution. Analysis and interpretation of the interpolated output data can be done on standard seismic workstations using section, map and 3D viewing and interpretation tools. The microlayer horizons themselves can be analyzed and interpreted in a new way, whereby the microlayer horizons are viewed in map view (see FIG. 8,9 and 10) or in 3D, and where the user can cycle through the stack of microlayer horizons to review changes along the microlayer horizons.
The generated output data highlights information about lateral variations in subsurface structure, stratigraphy, lithology and fluid distribution not directly apparent in the input seismic or seismic derived rock property data. Additionally, the generated output data contains filtered versions of the input seismic or seismic derived rock property data in which noise is reduced and therefore can be used to advantage in standard seismic or seismic derived rock property data analysis and interpretation.
The process is driven by a geologic model, which generally will not tend to capture detailed changes in subsurface structure, stratigraphy, lithology and fluid distribution. The process compensates for geometric inaccuracies in the geologic model by the specified angle perturbation procedure. In fact, useful data with the subject process may be generated based on a very simple model. In its most simple form such a model will consist of a layer bounded by two parallel horizons. The process can also be run without angle perturbation (angle range=0). In such a case the output can be used to assess the spatial changes in structure, stratigraphy, lithology and fluid distribution relative to the geometric model itself. In practical application the process may be applied consecutive times where the output is used to improve the geometric model, which in turn is used to generate improved data about spatial changes in subsurface structure, stratigraphy, lithology and fluid distribution.
The invention is particularly applicable to hydrocarbon exploration, development and production to determine the structure, stratigraphy, lithology and fluid distribution in hydrocarbon reservoirs and associated aquifers, and to determine fluid movement from seismic surveys repeated in time over a reservoir as it is depicted. The data generated by the process reveals how structure, stratigraphy, lithology and fluid distribution changes spatially, and how rapid such changes are. Rapid changes can point out overall reservoir boundaries or boundaries between different reservoir units and fluid contacts. More subtle changes can point to for example increasing or decreasing porosity and the % of hydrocarbon bearing rock relative to non-hydrocarbon bearing rock. Data generated by the process can reveal details about spatial changes in structure, stratigraphy, lithology and fluid distribution which are not easily detected when working with the seismic and seismic derived rock property data. Similarly, when the process is applied to repeated seismic surveys, enhanced detection of the movement in time of the fluid boundaries may be achieved.
The process is not limited to application in hydrocarbon exploration, production and development. Any analysis and interpretation of seismic or seismic derived rock property data with the purpose of determining subsurface structure, stratigraphy, lithology and fluid distribution may benefit from the process.